Understanding the Evolutionary Games in NSCLC Microenvironment

Abstract

While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking.Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naïve tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naïve cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.

Measuring competitive exclusion in non-small cell lung cancer

“…For example, clinical tumors are highly heterogeneous. Extending this work to include three 48 or more types will allow us to better model more clinically relevant resistance evolution. Finally, our results suggest that ecological effects are an important consideration in competition experiments, and continuing to show this empirically remains a priority going forward.…”

The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

“…This allows the physician to anticipate treatment-induced eco-evolutionary responses of cancer cells even before the treatment is applied in order to steer the eco-evolutionary dynamics of cancer cells during the course of the treatment [113,164]. Subsequent work focused on quantifying competitive release in NSCLC [68] and extended the original game to a game with three types of cancer cells [34]. A similar method was used to observe host-parasite-like interactions between cancer cell types due to paracrine behaviors [142].…”

Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer

“…In order to estimate the population of cancer cells of each tumor from the measurements of the longest diameter (LD), the tumor volume (V ) was calculated using the formula V = 0.5 • (LD) 3 , following common practice in tumor modelling when only a single tumor measurement is available [9]. Furthermore, as the range of tumor sizes wasvery large , going from 0 to 5926176 mm 3 , the cell populations were rescaled in the range 0 to 1, using the formula…”

“…Lung cancer is the second most common cancer and the leading cause of cancer death for both men and women and Non-Small Cell Lung Cancer (NSCLC) is the most frequent type of lung cancer, accounting for 84% of all lung cancer diagnoses [7]. Although novel anti-cancer therapies like targeted therapies and immunotherapy are allowing people with metastatic lung cancer to live longer than ever before, metastatic lung cancer remains incurable [7, 5]. This is often caused by the evolution of therapy resistance that the therapy selects for [23, 50, 40, 30].…”